Our first regularizer follows the intuition of wanting to minimize MI between subembeddings, similar to reducing total correlation of latent codes used by [1]}.
We follow the approach of [2]} for view selection and define the first regularizer as \(R_{\text{InfoMin}}=-\mathcal {L}_{\text{InfoMax}}\) , but suspect a priori that it may cause optimization issues since a lower bound is now being minimized instead of maximized.
\(R_{\text{InfoMin}} = \sum _{k\ne k^{\prime }}\sum _{i \in I} \log {\frac{\exp ({z_{i,k} \cdot z_{i,k^{\prime }} / \tau })}{\sum \limits _{a\in A}\exp ({z_{i,k} \cdot z_{a,k} / \tau })}}\) 
